World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
41
Citations
26335
World Ranking
1836
National Ranking
779

Xiao-Li Meng publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Xiao-Li Meng sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 144 publications — 35th percentile

35% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Xiao-Li Meng D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Xiao-Li Meng sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 41 D-Index — 49th percentile

49% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Overview

Xiao-Li Meng is affiliated with Harvard University in the United States and primarily works within the field of Mathematics. Their research contributions span several subfields, including Statistics and Probability, Artificial Intelligence, Epidemiology, Management Science and Operations Research, and Modeling and Simulation.

The scientist's work covers a range of topics, most notably Statistical Methods and Bayesian Inference, Statistical Methods and Inference, Advanced Statistical Methods and Models, COVID-19 epidemiological studies, Data-Driven Disease Surveillance, Vaccine Coverage and Hesitancy, and Statistics Education and Methodologies.

Frequent collaborators of Xiao-Li Meng include Ruobin Gong, Valerie C. Bradley, Shiro Kuriwaki, Michael Isakov, and Dino Sejdinović. The breadth of these collaborations indicates a consistent focus on interdisciplinary approaches to statistical research and applied science.

The scientist has published extensively in several venues, notably Harvard Data Science Review, arXiv (Cornell University), Statistical Science, Journal of the American Statistical Association, and The New England Journal of Statistics in Data Science. These publications reflect active engagement with both theoretical and applied statistics communities.

Selected recent publications include:

  • Unrepresentative big surveys significantly overestimated US vaccine uptake, 2021, Nature
  • The ASA president's task force statement on statistical significance and replicability, 2021, The Annals of Applied Statistics
  • Light-induced aryldifluoromethyl-sulfonylation/thioetherification of alkenes using arenethiolates as a photoreductant and sulfur source, 2023, Green Chemistry
  • Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss and Simpson's Paradox, 2021, Statistical Science
  • A Multi-resolution Theory for Approximating Infinite-p-Zero-n: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Tradeoff, 2020, Journal of the American Statistical Association

Best Publications

  • Handbook of Markov Chain Monte Carlo

    Steve Brooks;Andrew Gelman;Galin L. Jones;Xiao-Li Meng

  • Comparing correlated correlation coefficients

    Xiao-li Meng;Robert Rosenthal;Donald B. Rubin

  • POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES

    Andrew Gelman;Xiao-Li Meng;Hal Stern

  • Maximum likelihood estimation via the ECM algorithm: A general framework

    Xiao Li Meng;Donald B. Rubin

  • Simulating normalizing constants: from importance sampling to bridge sampling to path sampling

    Andrew Gelman;Xiao Li Meng

  • The Art of Data Augmentation

    David A van Dyk;Xiao-Li Meng

  • SIMULATING RATIOS OF NORMALIZING CONSTANTS VIA A SIMPLE IDENTITY: A THEORETICAL EXPLORATION

    Xiao-Li Meng;Wing Hung Wong

  • The EM Algorithm—an Old Folk‐song Sung to a Fast New Tune

    Xiao-Li Meng;David Van Dyk

  • Multiple-Imputation Inferences with Uncongenial Sources of Input

    Xiao-Li Meng

  • Factors affecting the detection of trends: Statistical considerations and applications to environmental data

    Gregory C. Reinsel;George C. Tiao;Xiao Li Meng

  • Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage

    John Barnard;Robert McCulloch;Xiao Li Meng

  • Using EM to Obtain Asymptotic Variance-Covariance Matrices: The SEM Algorithm

    Xiao-Li Meng;Donald B. Rubin

  • Posterior Predictive $p$-Values

    Xiao-Li Meng

  • Applications of multiple imputation in medical studies: from AIDS to NHANES

    John Barnard;Xiao Li Meng

  • Performing likelihood ratio tests with multiply-imputed data sets

    Xiao-Li Meng;Donald B. Rubin

  • Unrepresentative big surveys significantly overestimated US vaccine uptake

    Unknown

  • Significance levels from repeated p-values with multiply imputed data

    K H Li;X L Meng;T E Raghunathan;D B Rubin

  • Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election

    Xiao-Li Meng

  • To Center or Not to Center: That Is Not the Question—An Ancillarity–Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Efficiency

    Yaming Yu;Xiao-Li Meng

  • Seeking efficient data augmentation schemes via conditional and marginal augmentation

    X.-L. Meng;D. A. Van Dyk

  • Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family

    Andrew Gelman;Xiao-Li Meng

  • Handbook of Markov Chain Monte Carlo: Hardcover: 619 pages Publisher: Chapman and Hall/CRC Press (first edition, May 2011) Language: English ISBN-10: 1420079417

    Steve Brooks;Andrew Gelman;Galin Jones;Xiao-Li Meng

Frequent Co-Authors

Margarita Alegría
Margarita Alegría Harvard University
Donald B. Rubin
Donald B. Rubin Temple University
Andrew Gelman
Andrew Gelman Columbia University
Dan L. Nicolae
Dan L. Nicolae University of Chicago
Peter McCullagh
Peter McCullagh University of Chicago
David T. Takeuchi
David T. Takeuchi University of Washington
Jeremy J. Drake
Jeremy J. Drake Harvard University
Augustine Kong
Augustine Kong University of Oxford
Patrick E. Shrout
Patrick E. Shrout New York University
James S. Jackson
James S. Jackson University of Michigan–Ann Arbor

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